The rapid integration of the Internet of Things (IoT) into healthcare ecosystems has revolutionized patient monitoring and data accessibility; however, it has simultaneously expanded the cyber-attack surface, leaving sensitive medical data vulnerable to sophisticated breaches. This systematic literature review (SLR) addresses the critical challenge of balancing high-level security with the severe resource constraints of medical sensors and edge devices. By synthesizing evidence from 80 high-impact studies including 18 primary research articles published between 2022 and 2025 this paper evaluates the quality and efficacy of emerging cryptographic frameworks. The methodology utilizes a rigorous quality assessment framework to categorize research into "Strong," "Moderate," and "Weak" tiers. Key findings reveal a significant paradigm shift toward lightweight symmetric ciphers, such as GIFT and PRESENT, and certificateless authentication protocols like ELWSCAS, which reduce communication overhead in narrow-band environments. The analysis further explores the role of blockchain-assisted decentralization and DNA-based encryption in mitigating Single Point of Failure risks and providing high entropy. While decentralized models significantly enhance data integrity, they frequently encounter a scalability wall regarding transaction latency. Furthermore, the review assesses quantum readiness, noting that while lattice-based standards are being ported to microcontrollers, memory footprints remain a barrier for simpler sensors. Ultimately, this SLR maps the current technical frontiers and provides a strategic roadmap for future research, emphasizing the transition toward lightweight, quantum-resistant architectures as the next essential step in securing the global healthcare IoT infrastructure.
Conflict of Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Funding
The research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Data Fabrication/Falsification Statement
The author(s) declare that no data has been fabricated, falsified, or manipulated in this study.
Participant Consent
The authors confirm that Informed consent was obtained from all participants, and confidentiality was duly maintained.
Copyright and Licensing
For all articles published in the NIJEC journal, Copyright (c) of this study is with author(s).
The quantum threat to cybersecurity has accelerated the standardization of Post-Quantum Cryptography (PQC). Migrating legacy software to these quantum-safe algorithms is not a simple library swap, but a new software engineering challenge: existing vulnerability detection, refactoring, and testing tools are not designed for PQC's probabilistic behavior, side-channel sensitivity, and complex performance trade-offs. To address these challenges, this paper outlines a vision for a new class of tools and introduces the Automated Quantum-safe Adaptation (AQuA) framework, with a three-pillar agenda for PQC-aware detection, semantic refactoring, and hybrid verification, thereby motivating Quantum-Safe Software Engineering (QSSE) as a distinct research direction.
Alexandra González, Xavier Franch, Silverio Martínez-Fernández
Integrating Artificial Intelligence into Software Engineering (SE) requires having a curated collection of models suited to SE tasks. With millions of models hosted on Hugging Face (HF) and new ones continuously being created, it is infeasible to identify SE models without a dedicated catalogue. To address this gap, we present SEMODS: an SE-focused dataset of 3,427 models extracted from HF, combining automated collection with rigorous validation through manual annotation and large language model assistance. Our dataset links models to SE tasks and activities from the software development lifecycle, offering a standardized representation of their evaluation results, and supporting multiple applications such as data analysis, model discovery, benchmarking, and model adaptation.
Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources of foundational knowledge in SE, and (3) effectively detecting hallucinations on SE statements. The expected contributions include a variety of LLMs trained and evaluated on domain-specific datasets, new benchmarks on foundational knowledge in SE, and methods for detecting hallucinations. Initial results in terms of performance improvements on various non-code tasks are promising.
Proto-personas are commonly used during early-stage Product Discovery, such as Lean Inception, to guide product definition and stakeholder alignment. However, the manual creation of proto-personas is often time-consuming, cognitively demanding, and prone to bias. In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in terms of efficiency, effectiveness, user acceptance, and the empathy elicited by the generated personas. We conducted a case study with 19 participants embedded in a real Lean Inception, employing a qualitative and quantitative methods design. The results reveal the approach's efficiency by reducing time and effort and improving the quality and reusability of personas in later discovery phases, such as Minimum Viable Product (MVP) scoping and feature refinement. While acceptance was generally high, especially regarding perceived usefulness and ease of use, participants noted limitations related to generalization and domain specificity. Furthermore, although cognitive empathy was strongly supported, affective and behavioral empathy varied significantly across participants. These results contribute novel empirical evidence on how GenAI can be effectively integrated into software Product Discovery practices, while also identifying key challenges to be addressed in future iterations of such hybrid design processes.
UI automation is a useful technique for UI testing, bug reproduction, and robotic process automation. Recording user actions with an application assists rapid development of UI automation scripts, but existing recording techniques are intrusive, rely on OS or GUI framework accessibility support, or assume specific app implementations. Reverse engineering user actions from screencasts is non-intrusive, but a key reverse-engineering step is currently missing - recognizing human-understandable structured user actions ([command] [widget] [location]) from action screencasts. To fill the gap, we propose a deep learning-based computer vision model that can recognize 11 commands and 11 widgets, and generate location phrases from action screencasts, through joint learning and multi-task learning. We label a large dataset with 7260 video-action pairs, which record user interactions with Word, Zoom, Firefox, Photoshop, and Windows 10 Settings. Through extensive experiments, we confirm the effectiveness and generality of our model, and demonstrate the usefulness of a screencast-to-action-script tool built upon our model for bug reproduction.
Cross-organizational collaboration in Model-Based Systems Engineering (MBSE) faces many challenges in achieving semantic alignment across independently developed system models. SysML v2 introduces enhanced structural modularity and formal semantics, offering a stronger foundation for interoperable modeling. Meanwhile, GPT-based Large Language Models (LLMs) provide new capabilities for assisting model understanding and integration. This paper proposes a structured, prompt-driven approach for LLM-assisted semantic alignment of SysML v2 models. The core contribution lies in the iterative development of an alignment approach and interaction prompts, incorporating model extraction, semantic matching, and verification. The approach leverages SysML v2 constructs such as alias, import, and metadata extensions to support traceable, soft alignment integration. It is demonstrated with a GPT-based LLM through an example of a measurement system. Benefits and limitations are discussed.
Marco Autili, Martina De Sanctis, Paola Inverardi
et al.
As testified by new regulations like the European AI Act, worries about the human and societal impact of (autonomous) software technologies are becoming of public concern. Human, societal, and environmental values, alongside traditional software quality, are increasingly recognized as essential for sustainability and long-term well-being. Traditionally, systems are engineered taking into account business goals and technology drivers. Considering the growing awareness in the community, in this paper, we argue that engineering of systems should also consider human, societal, and environmental drivers. Then, we identify the macro and technological challenges by focusing on humans and their role while co-existing with digital systems. The first challenge considers humans in a proactive role when interacting with digital systems, i.e., taking initiative in making things happen instead of reacting to events. The second concerns humans having a reactive role in interacting with digital systems, i.e., humans interacting with digital systems as a reaction to events. The third challenge focuses on humans with a passive role, i.e., they experience, enjoy or even suffer the decisions and/or actions of digital systems. The fourth challenge concerns the duality of trust and trustworthiness, with humans playing any role. Building on the new human, societal, and environmental drivers and the macro and technological challenges, we identify a research roadmap of digital systems for humanity. The research roadmap is concretized in a number of research directions organized into four groups: development process, requirements engineering, software architecture and design, and verification and validation.
In this paper, we first define the notion of finitely-cosmall quotient (singly-cosmall quotient) morphisms. Then we give a characterization of this new concept. We show that an epimorphism p:Y→U is a finitely-cosmall quotient (singly-cosmall quotient) if and only if for any right R-module Z any morphism g:Z→Y such that pg is a finitely-copartial isomorphism (singly-copartial isomorphism) from Z to Y with codomain U is a finitely (singly) split epimorphism. We also investigate the relation between pure-cosmall quotient and finitely-cosmall quotient (singly-cosmall quotient) morphisms. We prove that over a right Noetherian ring R, an epimorphism p:Y→U is a pure-cosmall quotient morphism if and only if p is a finitely-cosmall quotient (singly-cosmall quotient) morphism. Moreover, we obtain an example of right minimal morphisms by using finitely-cosmall quotient (singly-cosmall quotient) morphisms.
Confronting the age of artificial intelligence, exploring art through technology has become one of the directions of interdisciplinary development. Not only does artificial intelligence technology explore sustainability on a technical level; it can also take advantage of itself to focus on the visual perception of the living environment. People frequently interpret environmental features through their eyes, and the use of intuitive eye-tracking can provide effective data that can contribute to environmental sustainability in managing the environment and color planning to enhance the image of cities. This research investigates the visual responses of people viewing the historic city of Macau through an eye movement experiment to understand how the color characteristics of the physical environment are perceived. The research reveals that the buildings and plantings in the historic district of Macau are the most visible objects in the environment, while the smaller scale of St. Dominic’s Square, the Company of Jesus Square, and St. Augustine’s Square, which have a sense of spatial extension, have also become iconic environmental landscapes. This also draws visual attention and guides the direction of travel. The overall impressions of the Historic Centre of Macau, as expressed by the participants after the eye movement experiment, were mainly described as “multiculturalism”, “architectural style”, “traditional architecture”, “color scheme”, and “garden planting”. The 60 colors representing the urban color of Macau are then organized around these deep feelings about the environment. Therefore, for future inspiration, the 60 colors can be applied through design practice to create color expressions that fit the local characteristics, and thereby enhance the overall visual image of the city.
Alla G. Polyakova, Anna G. Soloveva, Petr V. Peretyagin
et al.
The development of anti-pain technologies in the complex treatment of pain syndromes is one of the most urgent tasks of modern medicine. We undertook a placebo-controlled experimental study of the therapeutic potential of low-intensity laser radiation when applied to acupuncture points that are directly related to the autonomic nervous system. The adaptation effect of puncture photobiomodulation on the induction of stress-mediated autonomic reactions, oxidative metabolism and microcirculation in animals during the acute phase of pain stress was revealed. The data obtained are of interest for use in the complex rehabilitation of patients with pain syndromes.
With the development of the hydrogen fuel cell automobile industry, higher requirements are put forward for the construction of hydrogen energy infrastructure, the matching of parameters and the control strategy of hydrogen filling rate in the hydrogenation process of hydrogenation station. Fuel for hydrogen fuel cell vehicles comes from hydrogen refueling stations. At present, the technological difficulty of hydrogenation is mainly reflected in the balanced treatment of reducing the temperature rise of hydrogen and shortening the filling time during the fast filling process. The Joule-Thomson (JT) effect occurs when high-pressure hydrogen gas passes through the valve assembly, which may lead to an increase in hydrogen temperature. The JT effect is generally reflected by the JT coefficient. According to the high pressure hydrogen in the pressure reducing valve, the corresponding JT coefficients were calculated by using the VDW equation, RK equation, SRK equation and PR equation, and the expression of JT effect temperature rise was deduced, which revealed the hydrogen temperature variation law in the process of reducing pressure. Make clear the relationship between charging parameters and temperature rise in the process of decompression; the flow and thermal characteristics of hydrogen in the process of decompression are revealed. This study provides basic support for experts to achieve throttling optimization of related pressure control system in hydrogen industry.
Large Language Models (LLMs) have shown great potential in solving complex problems in various fields, including oil and gas engineering and other industrial engineering disciplines like factory automation, PLC programming etc. However, automatic identification of strong and weak solutions to fundamental physics equations governing several industrial processes remain a challenging task. This paper identifies the limitation of current LLM approaches, particularly ChatGPT in selected practical problems native to oil and gas engineering but not exclusively. The performance of ChatGPT in solving complex problems in oil and gas engineering is discussed and the areas where LLMs are most effective are presented.
[Background.] Empirical research in requirements engineering (RE) is a constantly evolving topic, with a growing number of publications. Several papers address this topic using literature reviews to provide a snapshot of its "current" state and evolution. However, these papers have never built on or updated earlier ones, resulting in overlap and redundancy. The underlying problem is the unavailability of data from earlier works. Researchers need technical infrastructures to conduct sustainable literature reviews. [Aims.] We examine the use of the Open Research Knowledge Graph (ORKG) as such an infrastructure to build and publish an initial Knowledge Graph of Empirical research in RE (KG-EmpiRE) whose data is openly available. Our long-term goal is to continuously maintain KG-EmpiRE with the research community to synthesize a comprehensive, up-to-date, and long-term available overview of the state and evolution of empirical research in RE. [Method.] We conduct a literature review using the ORKG to build and publish KG-EmpiRE which we evaluate against competency questions derived from a published vision of empirical research in software (requirements) engineering for 2020 - 2025. [Results.] From 570 papers of the IEEE International Requirements Engineering Conference (2000 - 2022), we extract and analyze data on the reported empirical research and answer 16 out of 77 competency questions. These answers show a positive development towards the vision, but also the need for future improvements. [Conclusions.] The ORKG is a ready-to-use and advanced infrastructure to organize data from literature reviews as knowledge graphs. The resulting knowledge graphs make the data openly available and maintainable by research communities, enabling sustainable literature reviews.
Composite slab systems have become increasingly popular over the last few decades because of the advantages of merging the two building materials, profiled steel sheets and concrete. The profiled composite slab’s performance depends on the composite interaction at the longitudinal direction of the concrete–steel interface. Geopolymer concrete has emerged over the last few years as a potential sustainable construction material, with 80% less carbon dioxide emissions than cementitious concrete. Recently, self-compacted geopolymer concrete (SCGC) has been developed, synthesised from a fly ash/slag ratio equal to 60/40, micro fly ash (5%), anhydrous sodium metasilicate solid powder as the alkali-activator and a water/solid content ratio equal to 0.45. The production of SCGC eliminates the need for an elevated temperature during curing and high corrosive alkali-activator solutions, as in traditional geopolymer concrete. The bond characteristics of the profiled composite slab system incorporated with the SCGC mix have not yet been thoroughly investigated. The cost-effectiveness of small-scale tests has popularised its usage by many researchers as an alternative technique to large-scale testing for assessing composite slab load shear capacity. In this paper, small-scale push tests were conducted to investigate the load slip behaviour of the SCGC composite slab compared to the normal concrete (NC) composite slab, with targeted compressive strengths of 40 and 60 MPa. The results indicate that SCGC has better chemical adhesion with profiled steel sheets than NC. Additionally, the profiled composite slab incorporated with SCGC possesses higher ultimate strength and toughness than the normal concrete composite slab.
Despite the tremendous impact that a good constitutive relation for the response of arterial tissues can have with regard to advances in cardiovascular science and medicine, and notwithstanding the intense effort to put a felicitous constitutive relation into place, no reliable constitutive relation is available in the literature. In this review article, we provide a brief survey and assessment of the evolution of constitutive relations that have been developed to describe the response of arterial tissues, their inadequacies, and the various quintessential aspects of the response that need to be taken into consideration. We then fashion a nonlinear constitutive relation to describe an inhomogeneous anisotropic compressible viscoelastic solid, which while being grossly inadequate to describe the tissue in its entirety, makes it evident that what one ought to strive for is not in capturing the complexity of tissues, but rather the development of a simple global measure that can be a reliable predictor of the onset of tissue disease, and tissue damage and failure.
Exploring a pipeline’s response to blast vibration during tunnel excavation is critical for ensuring the safety of the pipeline. In this paper, the vibration monitoring and numerical simulation methods are used to evaluate the dynamic response of ground soil and pipelines to blasts. The attenuation law of peak particle velocity (<i>PPV</i>) and the distribution characteristics of peak effective stress (PES) in pipe sections under different working conditions are studied. The following findings are recorded: (1) A three-dimensional model considering in situ stress is established, and it is found the triangular equivalent load simulation blast effect method used in this paper can effectively reflect the impact of blasting on pipelines. The simulation error is controlled at 7.69%. (2) The ground <i>PPV</i> of each monitoring point decays continuously with the increase in horizontal and axial distance, and the cavity enlargement effect is exhibited above the excavation area. The oncoming blast side <i>PPV</i> of the pipe section is more significant than that behind the blast side. (3) When the blast vibration is transmitted to the pipe, there are differences in the <i>PPV</i> and PES distribution characteristics across the pipe cross-section. The <i>PPV</i> is greater in the lower part of the pipe section, while the PES value is greater in the upper part of the pipe section. The maximum PES of 1.53 MPa is significantly lower than the safety threshold (≤4.6 MPa) at the hazardous-section-monitoring point. (4) A pipeline <i>PPV</i> prediction model is proposed to guide subsequent blasting program development. An empirical formula for the safety criterion applicable to this study is proposed for the scientific implementation of safety assessments for subsequent construction. This safety evaluation framework can be used as a reference for similar projects.
A method of Sequential Log-Convex Programming (SLCP) is constructed that exploits the log-convex structure present in many engineering design problems. The mathematical structure of Geometric Programming (GP) is combined with the ability of Sequential Quadratic Program (SQP) to accommodate a wide range of objective and constraint functions, resulting in a practical algorithm that can be adopted with little to no modification of existing design practices. Three test problems are considered to demonstrate the SLCP algorithm, comparing it with SQP and the modified Logspace Sequential Quadratic Programming (LSQP). In these cases, SLCP shows up to a 77% reduction in number of iterations compared to SQP, and an 11% reduction compared to LSQP. The airfoil analysis code XFOIL is integrated into one of the case studies to show how SLCP can be used to evolve the fidelity of design problems that have initially been modeled as GP compatible. Finally, a methodology for design based on GP and SLCP is briefly discussed.